[R-sig-Geo] geostatistic memory issue

[Fernando Santo]

Dear Robert,

Thanks a lot for your attention.  I downloaded the raster package and 
manual.  I am still digesting all this information...
I just tried to do a quick adaptation in my code and it did not worked.  
I am doing something wrong...

Fernando

# read the data
dat <- read.csv("/home/fernando/Desktop/data.csv")

library(fields)

# I just left how as before to make the fitting between the range of x and y
col_xcoord <- seq(723606,724902,by=4)        #  correct: 325
row_ycoord <- seq(9661022,9662346,by=4)   #  correct: 332
xmin = min(col_xcoord)
ymin = min(row_ycoord)
locs.x = col_xcoord - xmin
locs.y = row_ycoord - ymin
newlocs = expand.grid(locs.x,locs.y)

locs = data.frame(dat$long-xmin,dat$lat-ymin)  # adjusted location as 
data frame
sqrt.gf = sqrt(dat$gap.frac)     # response variable

fit = Krig(locs,sqrt.gf,theta=24.15)

#####  then I used your suggestion
library(raster)
# create a target RasterLayer
r <- raster(xmn=723604,xmx=724904,ymn=9661020,ymx=9662348)
res(r) <- 4

# Sure It seems not correct....
x <- interpolate(r, fit)
Error in if (dataclasses != "try-error") { : argument is of length zero




> Fernando,
>
> You can use the raster package for dealing with memory issues like this:
>
> library(fields)
> fit<- Krig(ozone$x, ozone$y, theta=20)
>
> library(raster)
> # create a target RasterLayer
> r<- raster(xmn=min(ozone$x), xmx=max(ozone$x), ymn=min(ozone$y),
> ymx=max(ozone$y))
> res(r)<- 1/6
> x<- interpolate(r, fit)
> plot(x)
> x
>
>
> in your case, that would be something like this:
> ...
> r<- raster(xmn=723604,xmx=724904,ymn=9661020,ymx=9662348)
> res(r)<- 4
> ...
>
> Robert
>
> On Mon, Nov 29, 2010 at 3:54 PM, Fernando Santo<fdbesanto at gmail.com>  wrote:
>> Dear All,
>>
>> I am running a geostatistical analysis using the package "fields" of
>> n=980 and grid of approximately 1x1 km with 4 m of spatial resolution.
>> Because the fine grid resolution of 4 meters, which I really need for my
>> experiment, I am having problems with memory allocation.  I am using an
>> OS linux 32 bit (i686-pc-linux-gnu) with 4 Gb of memory and 2 Gb of swap
>> and R version 2.12.0.  Running the R command gc() in my computer, I have
>> the following:
>>
>>   >gc()
>>              used        (Mb)   gc      trigger      (Mb)      max used
>> (Mb)
>> Ncells   147153      4.0              350000      9.4           350000   9.4
>> Vcells    86617       0.7              786432       6.0
>> 434604   3.4
>>
>> I tried to work around of my memory issue (?Memory) for Linux, start R
>> with the command *R --min-vsize=100M --max-vsize=3G --min-nsize=100M
>> --max-nsize=3G*, but still I did not saved the problem of memory
>> allocation.  I know that the solution could be: (1) install a OS 64bits
>> or (2) reduce the spatial resolution of my grid (e.g. for 10x10 m).
>> However, I can not format my computer right now and I need a grid of 4 x
>> 4 m.
>>
>> I would like to ask you the following:
>> 1 - How I could increase the memory allocation of my linux?  Is there
>> something wrong with my work around of memory allocation showed above?
>> 2 - Is possible to apply the use of the R package  e.g. "bigmemory" in
>> my geostatistic script (please see bellow)?  If yes, how it could be?
>> My problem is only in the last command of my script when I call the
>> function "predict" of fields.
>>
>> My script is bellow:
>>
>> Any help will be very welcome.
>> Thanks for your attention.
>> Fernando
>>
>> ###########################################
>> dat<- read.csv("/home/fernando/Desktop/data.csv")
>> library(fields)
>>
>> # set  x-y  coordinates of image, considering the center of each pixel
>> col_xcoord<- seq(723606,724902,by=4)         #  correct: 325
>> row_ycoord<- seq(9661022,9662346,by=4)   #  correct: 332
>> xmin = min(col_xcoord)
>> ymin = min(row_ycoord)
>> locs.x = col_xcoord-xmin
>> locs.y = row_ycoord-ymin
>> newlocs = expand.grid(locs.x,locs.y)
>>
>> locs = data.frame(dat$long-xmin,dat$lat-ymin)  # adjusted location as
>> data frame
>> sqrt.gf = sqrt(dat$gap.frac)                                   # The
>> response variable
>>
>> #################################################
>> # Produce the Kriging estimates using exponential variogram with scale
>> parameter 24.15
>> #################################################
>>
>> fit = Krig(locs,sqrt.gf,theta=24.15)
>> # I used the range parameter 24.15 estimated earlier
>>
>> summary(fit) # summary of fit
>>
>> # HERE I HAD THE ERROR OF MEMORY ALLOCATION
>> # "predict" the kriging values
>> krig.out = predict(fit,newlocs)
>> # Error: cannot allocate vector of size 806.7 Mb
>>
>> # This takes about 2 minutes on Mac of my friend
>> # but it did not work in my linux
>>
>>
>>
>>
>>
>>         [[alternative HTML version deleted]]
>>
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